{"id":"W3113785905","doi":"10.48550/arxiv.2012.15477","title":"Particle Dual Averaging: Optimization of Mean Field Neural Networks with Global Convergence Rate Analysis","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Stochastic Gradient Optimization Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Convergence (economics); Artificial neural network; Rate of convergence; Mathematical optimization; Nonlinear system; Computer science; Empirical risk minimization; Inner loop; Applied mathematics; Mathematics; Artificial intelligence; Physics; Key (lock)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001336386,0.0002359572,0.0003634034,0.0001231016,0.00007487828,0.00007602108,0.0008991478,0.0001399488,0.00003501731],"category_scores_gemma":[0.00003820618,0.0002566934,0.0001626565,0.002341787,0.00008576888,0.0002794245,0.0008639697,0.0002280606,0.000001537266],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008938578,"about_ca_system_score_gemma":0.00008015096,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001500514,"about_ca_topic_score_gemma":0.00002459622,"domain_scores_codex":[0.9984524,0.000134475,0.0002428242,0.0008155479,0.0001133828,0.000241332],"domain_scores_gemma":[0.9983782,0.00009129499,0.0003722996,0.0007426106,0.0002599035,0.0001556967],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004391847,0.00003036985,0.01211958,0.00001362965,0.0002468697,0.00006168452,0.0001195796,0.9485744,0.000001394397,0.03869438,0.00002307415,0.0000711285],"study_design_scores_gemma":[0.000234352,0.0001184639,0.0006495407,0.00002033656,0.0003556182,0.000001723649,0.00002326494,0.9965736,0.0001316864,0.001640569,9.783198e-7,0.0002498775],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02146819,0.00001696573,0.9775102,0.0001916823,0.0001639533,0.0002257265,0.000008064431,0.0002943763,0.0001208024],"genre_scores_gemma":[0.9600177,0.00003051166,0.03973205,0.0001433694,0.00001838487,0.000001165552,0.00002058763,0.000007946282,0.00002831803],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9385495,"threshold_uncertainty_score":0.9999886,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04547622419307678,"score_gpt":0.1913538874143895,"score_spread":0.1458776632213127,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}